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Identification of CDK1 As a Candidate Marker in Cutaneous Squamous Cell Carcinoma by Integrated Bioinformatics Analysis

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Identification of CDK1 As a Candidate Marker in Cutaneous Squamous Cell Carcinoma by Integrated Bioinformatics Analysis 478 Original Article Identification of CDK1 as a candidate marker in cutaneous squamous cell carcinoma by integrated bioinformatics analysis Si Qin1,2#, Yu Yang2,3#, Hao-Bin Zhang4, Xiao-Huan Zheng5, Hua-Run Li1,2, Ju Wen1,2 1Department of Dermatology, Guangdong Second Provincial General Hospital, Guangzhou, China; 2The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China; 3Department of Urology, Peking University Shenzhen Hospital, Shenzhen, China; 4The Big Data Institute, Guangdong Create Environmental Technology Company Limited, Guangzhou, China; 5Nanhai District People’s Hospital, Foshan, China Contributions: (I) Conception and design: S Qin, Y Yang, HB Zhang; (II) Administrative support: J Wen; (III) Provision of study materials or patients: S Qin; (IV) Collection and assembly of data: XH Zheng, HR Li; (V) Data analysis and interpretation: S Qin, Y Yang, HB Zhang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #The authors contributed equally to this work. Correspondence to: Ju Wen. Department of Dermatology, Guangdong Second Provincial General Hospital, Guangzhou 510317, China. Email: [email protected]. Background: Cutaneous squamous cell carcinoma (cSCC) is a relatively common cancer that accounts for nearly 50% of non-melanoma skin cancer cases. However, the genotypes that are linked with poor prognosis and/or high relapse rates and pathogenic mechanisms of cSCC are not fully understood. To address these points, three gene expression datasets were analyzed to identify candidate biomarker genes in cSCC. Methods: The GSE117247, GSE32979, and GSE98767 datasets comprising a total of 32 cSCC samples and 31 normal skin tissue samples were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified and underwent pathway enrichment analyses with the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). A putative DEG protein–protein interaction (PPI) network was also established that included hub genes. The expression of CDK1, MAD2L1, BUB1 ans CDC20 were examined in the study. Results: A total of 335 genes were identified, encompassing 219 found to be upregulated and 116 genes that were downregulated in cSCC, compared to normal tissue. Enriched functions of these DEGs were associated with Ephrin receptor signaling and cell division; cytosol, membrane, and extracellular exosomes; ATP-, poly(A) RNA-, and identical protein binding. We also established a PPI network comprising 332 nodes and identified KIF2C, CDC42, AURKA, MAD2L1, MYC, CDK1, FEN1, H2AFZ, BUB1, BUB1B, CKS2, CDC20, CCT2, ACTR2, ACTB, MAPK14, and HDAC1 as candidate hub genes. The expression of CDK1 are significantly higher in the cSCC tissues than that in normal skin. Conclusions: The DEGs identified in this study are potential therapeutic targets and biomarkers for cSCC. CDK1 is a gene closely related to the occurrence and development of cSCC, which may play an important role. Bioinformatics analysis shows that it is involved in the important pathway of the pathogenesis of cSCC, and may be recognized and applied as a new biomarker in the future diagnosis and treatment of cSCC. Keywords: Cluster analysis; carcinoma; squamous cell; critical pathways; genetic association studies Submitted Sep 18, 2020. Accepted for publication Nov 12, 2020. doi: 10.21037/tcr-20-2945 View this article at: http://dx.doi.org/10.21037/tcr-20-2945 © Translational Cancer Research. All rights reserved. Transl Cancer Res 2021;10(1):469-478 | http://dx.doi.org/10.21037/tcr-20-2945 470 Qin et al. Integrated bioinformatics analysis for cutaneous squamous cell carcinoma Introduction of the study. We present the following article in accordance with Cancer is a major threat to human health and a global the MDAR reporting checklist (available at http://dx.doi. economic burden. The economic impact associated org/10.21037/tcr-20-2945). with anti-cancer therapies can largely affect treatment access and continuity, increasing the likelihood of poor prognoses (1). Cutaneous squamous cell carcinoma (cSCC) Methods is a relatively common cancer that accounts for nearly Microarray analysis and identification of DEGs 50% of non-melanoma skin cancer cases (2). In addition to environmental exposure, a variety factors such as aging, The three microarray datasets of primary cSCC and normal human papilloma virus infection, immunosuppression, tissues (GSE117247, GSE32979, and GSE98767) were genetic factors and in particular, ultraviolet radiation obtained from the NCBI GEO database. The GSE117247 are all associated with increased cSCC risk. At present data was based on the Affymetrix Human Genome U95 the preferred treatment of cSCC is radical resection. v2 array (GPL8300 platform), derived from eight primary Although most cSCC patients who undergo cruative cSCC and nine normal skin samples. The GSE32979 surgery exhibit favorable prognoses, a minority develop microarray data were obtained using the Illumina Human-6 metastasis or recurrence that is potentially fatal. For v2.0 expression bead chip (GPL6102 platform) from 15 patients with advanced cSCC, due to the lack of large- cSCC and 13 normal skin samples. GSE98767 data were scale clinical trials, clinicians have to rely on the efficacy obtained using the Illumina Human HT-12 v4.0 expression evidence of other tumor types (such as head and neck bead chip (GPL10558 platform) from nine primary cSCC mucosal squamous cell carcinoma) to guide treatment and nine normal tissue samples. The integration and plan. Systemic therapy, like platinum chemotherapy, were analysis of all three datasets was performed with GEO2R, used in advanced cSCC patients population (3,4). Genetic a web-based analysis tool [https://www.ncbi.nlm.nih. alterations have shown to play a key role in the resistance gov/geo/geo2r/?acc=GSE98767], and saved.. Genes to anticancer drugs in oncology patients, demonstrating differentially expressed between cSCC and normal tissues an urgent need to identify key genes that can serve as were screened according to a standard P value of <0.05, and therapeutic targets and biomarkers for monitoring the fold change (FC) in gene expression >0 (|logFC| >0). The response to treatment (5). Recently, EGFR inhibitors, DEGs were categorised into Venn diagrams using a web- cemiplimab and pembrolizumab which aim at biomarkers based application (http://bioinformatics.psb.ugent.be/ were used for patients with advanced cSCC. webtools/Venn/). The study was conducted in accordance The development of microarray and high-throughput with the Declaration of Helsinki (as revised in 2013). sequencing technologies has facilitated the identification of biomarkers related to tumor occurrence and progression GO and KEGG pathway enrichment analyses (6-8). Unlike head and neck squamous cell carcinoma, relatively few marker genes have been identified in cSCC The online gene function annotation tool, Database for (9,10). Annotation, Visualization, and Integrated Discovery In recent research, Wei et al. (11) suggested that EGR3 (DAVID), (https://david.ncifcrf.gov/), was used to analyze had the potential to help in the diagnosis and predictive the functions and pathway enrichment of candidate DEGs. treatments of CSCC by bioinformatics, but the samples in The cut-off for calculating the degree of enrichment was the study are not from the same organization or the same P<0.05. GEO profiles. To address this deficiency, bioinformatic analysis was performed in our study for providing more PPI networks, clustering, and identification of hub genes reliable research evidence for the early diagnosis and prevention of cSCC the development and progression. In Proteins corresponding to DEGs and a PPI network our study, we used the same tissue-derived specimens, and diagram were obtained with STRING (https://string- compare the different genes in the same GEO profile which db.org), an online tool for identifying functional protein helped to avoid partial bias. In addition, we validated the association networks. Candidate genes were evaluated results of bioinformatics in order to enhance the reliability with the Cytoscape (Version 3.7.2) software to analyze © Translational Cancer Research. All rights reserved. Transl Cancer Res 2021;10(1):469-478 | http://dx.doi.org/10.21037/tcr-20-2945 Translational Cancer Research, Vol 10, No 1 January 2021 471 GSE117247 GSE32979 GSE117247 GSE32979 for 3–5 min, followed by rinsing with running water for 5–10 min to stop the color development. Then, the tissue 388 416 sections were counterstained with hematoxylin for 1 min 566 2334 663 2883 and differentiated with 0.5% hydrochloric alcohol for 219 116 2 seconds. The sections were dehydrated and mounted 236 985 161 822 for microscopic examination at room temperature. The signal was visualized with Light microscopy (Olympus 3236 3523 BX43; Olympus Corporation) (magnification, ×200) was GSE98767 GSE98767 performed, and the presence of brown granules in the cytoplasm was considered a positive signal. up-regulation genes down-regulation genes Figure 1 DEGs of three GEO profiles. Detailed explanation: 219 Results upregulated genes and 116 downregulated genes were filtered from three GEO profiles with LogFC >0 and LogFC <0. DEGs, Identification of DEGs in cSCC differentially expressed genes; GEO, Gene Expression Omnibus. Gene expression profiles of 32 cSCC and 31 normal skin tissue samples were compared in this study. GEO2R interactions between genes. All analyzed
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